A Multi-Agent System for Visualization Simulated User Behaviour

1. A Multi-Agent System for Visualization Simulated User Behaviour B. de Vries, J. Dijkstra

2. Agenda VR-DIS research programme: B. de Vries AI for visualization of human behavior: J. Dijkstra

3. VR Technology in (Architectural) Design • Traditional process and use • Envisioned process and use

4. Traditional process: Sketch • Paper & Pencil • Reflection on Thoughts • Vague

5. Traditional process: Design • 2D/3D Modeling • Material use • Consultancy: Installation, Construction, etc.

6. Traditional process: Presentation • Convey design • Impression of building

7. Envisioned process: 3D Modeling • Direct manipulation • Implicit relations • Sculpturing

8. Envisioned process: Scene Painting • Realistic images • No construction material

9. Envisioned process: Evaluation • Indoor climate • Lighting • Structural behavior • Acoustics • User behavior

10. Example: Urban plan

11. Towards a Multi-Agent System for Visualizing Simulated User Behavior

12. Introduction of the Model

13. Architects and urban planners are often faced with the problem to assess how their design or planning decisions will affect the behavior of individuals. • One way of addressing this problem is the use of models simulating the navigation of users in buildings and urban environments. A Multi-Agent System based on Cellular Automata

14. Essentials of Cellular Automata

15. Cellular automata are discrete dynamical systems whose behavior is completely specified in terms of a local relation Cellular automata are characterized by the following features: • Grid • Time • Cell • State

16. Cellular Automata Model of Traffic Flow

17. Agent Characteristics

18. Agent Definitions Agents are computational systems that inhibit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed (Maes). An autonomous agent is a system situated within and part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda (Franklin & Graesser).

19. Agent Properties • Autonomy - agents have some control over their actions and internal state • Social ability - agents interact with other agents • Reactivity - agents perceive their environment and respond to changes in it • Pro-activeness - agents exhibit goal-directed behavior by acting on their own initiative • ? Mentalistic capabilities - knowledge, belief, intention, emotion

20. Agent Architecture State Perception Action Sensors Effectors Production System

21. Multi Agent Simulation Models

22. Offers the promise of simulating autonomous agents and the interaction between them. behaviors evolve dynamically during the simulation • Evolution capabilities: • evolution of the agent’s environment • evolution of the agent’s behavior during the simulation • anticipated behavior • unplanned behavior

23. Towards the Framework

24. Artificial Intelligence Cellular Automata Distributed Artificial Intelligence Multi Agent Simulation Models

25. Motivation • Develop a system how people move in a particular environment. • People are represented by agents. • The cellular automata model is used to simulate their behavior across the network. • A simulation system would allow the designer to assess how its design decisions influence user movement and hence performance indicators.

26. Network Model The network is the three-dimensional cellular automata model representation of a state at a certain time.

27. different neighborhoods

28. transition of a state of a cell

29. Agent Model

30. User Agent Define an user-agent as: U = < R | S >, where: • R is finite set of role identifiers; {actor, subject} • Sscenario , defined by: S = <B, I, A, F, T>, where: • B represents the behavior of user-agent i • I represents the intentions of a user-agent i • A represents the activity agenda user user-agent i • F represents the knowledge of information about the environment, called Facets • T represents the time-budget each user-agent possesses

31. The Integration of Cellular Automata and Multi Agent Technology Initially, we will realize different graphic representations of our simulation: • a network-based view • a main node-based view • an actor-based view

32. network grid and decision points

33. main node-based view

34. actor-based view / network-based view

35. Simulation Experiment Design of a simulation experiment of pedestrian movement. Considering a T-junction walkway where pedestrians will be randomly created at one of the entrances. Some impressions ...

39. Demo

40. Conclusions

41. Complex behavior can be simulated by using the concept of cellular automata in the context of multi-agent technology. • The development of multi-agent models offers the promise of simulating autonomous individuals. • A multi-agent model can be used for visualizing simulated user behavior to support the assignment of design performance. • The proposed concept potentially has a lot to offer in architecture and urban planning when visual and active environments may impact user behavior and decision-making processes.